Yuepeng Wang

Lei Shi, Yuepeng Wang, Rajeev Alur et al.

Debugging imperative network programs is a challenging task for developers because understanding various network modules and complicated data structures is typically time-consuming. To address the challenge, this paper presents an automated technique for repairing network programs from unit tests. Specifically, given as input a faulty network program and a set of unit tests, our approach localizes the fault through symbolic reasoning, and synthesizes a patch such that the repaired program can pass all unit tests. It applies domain-specific abstraction to simplify network data structures and utilizes modular analysis to facilitate function summary reuse for symbolic analysis. We implement the proposed techniques in a tool called NetRep and evaluate it on 10 benchmarks adapted from real-world software-defined networking controllers. The evaluation results demonstrate the effectiveness and efficiency of NetRep for repairing network programs.

Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen et al.

Noise poses a challenge for learning dynamical-system models because already small variations can distort the dynamics described by trajectory data. This work builds on operator inference from scientific machine learning to infer low-dimensional models from high-dimensional state trajectories polluted with noise. The presented analysis shows that, under certain conditions, the inferred operators are unbiased estimators of the well-studied projection-based reduced operators from traditional model reduction. Furthermore, the connection between operator inference and projection-based model reduction enables bounding the mean-squared errors of predictions made with the learned models with respect to traditional reduced models. The analysis also motivates an active operator inference approach that judiciously samples high-dimensional trajectories with the aim of achieving a low mean-squared error by reducing the effect of noise. Numerical experiments with high-dimensional linear and nonlinear state dynamics demonstrate that predictions obtained with active operator inference have orders of magnitude lower mean-squared errors than operator inference with traditional, equidistantly sampled trajectory data.

Yuepeng Wang, Yu Feng, Ruben Martins et al.

In many common scenarios, programmers need to implement functionality that is already provided by some third party library. This paper presents a tool called Hunter that facilitates code reuse by finding relevant methods in large code bases and automatically synthesizing any necessary wrapper code. The key technical idea underlying our approach is to use types to both improve search results and guide synthesis. Specifically, our method computes similarity metrics between types and uses this information to solve an integer linear programming (ILP) problem in which the objective is to minimize the cost of synthesis. We have implemented Hunter as an Eclipse plug-in and evaluate it by (a) comparing it against S6, a state-of-the-art code reuse tool, and (b) performing a user study. Our evaluation shows that Hunter compares favorably with S6 and significantly increases programmer productivity.